161 related articles for article (PubMed ID: 17822265)
1. Combination of genetic algorithm and partial least squares for cloud point prediction of nonionic surfactants from molecular structures.
Ghasemi J; Ahmadi S
Ann Chim; 2007; 97(1-2):69-83. PubMed ID: 17822265
[TBL] [Abstract][Full Text] [Related]
2. Genetic Algorithm and Self-Organizing Maps for QSPR Study of Some N-aryl Derivatives as Butyrylcholinesterase Inhibitors.
Ahmadi S; Ganji S
Curr Drug Discov Technol; 2016; 13(4):232-253. PubMed ID: 27457492
[TBL] [Abstract][Full Text] [Related]
3. A review on progress in QSPR studies for surfactants.
Hu J; Zhang X; Wang Z
Int J Mol Sci; 2010 Mar; 11(3):1020-1047. PubMed ID: 20479997
[TBL] [Abstract][Full Text] [Related]
4. Application of genetic algorithm-kernel partial least square as a novel non-linear feature selection method: partitioning of drug molecules.
Noorizadeh H; Sobhan Ardakani S; Ahmadi T; Mortazavi SS; Noorizadeh M
Drug Test Anal; 2013 Feb; 5(2):89-95. PubMed ID: 21438162
[TBL] [Abstract][Full Text] [Related]
5. A quantitative structure property relationship for prediction of solubilization of hazardous compounds using GA-based MLR in CTAB micellar media.
Ghasemi JB; Abdolmaleki A; Mandoumi N
J Hazard Mater; 2009 Jan; 161(1):74-80. PubMed ID: 18456399
[TBL] [Abstract][Full Text] [Related]
6. Prediction and application in QSPR of aqueous solubility of sulfur-containing aromatic esters using GA-based MLR with quantum descriptors.
Yin C; Liu X; Guo W; Lin T; Wang X; Wang L
Water Res; 2002 Jul; 36(12):2975-82. PubMed ID: 12171394
[TBL] [Abstract][Full Text] [Related]
7. Partial least squares modeling and genetic algorithm optimization in quantitative structure-activity relationships.
Hasegawa K; Funatsu K
SAR QSAR Environ Res; 2000; 11(3-4):189-209. PubMed ID: 10969871
[TBL] [Abstract][Full Text] [Related]
8. Quantitative structure/property relationship analysis of Caco-2 permeability using a genetic algorithm-based partial least squares method.
Yamashita F; Wanchana S; Hashida M
J Pharm Sci; 2002 Oct; 91(10):2230-9. PubMed ID: 12226850
[TBL] [Abstract][Full Text] [Related]
9. Prediction of critical micelle concentration of nonionic surfactants by a quantitative structure - property relationship.
Mozrzymas A; Rózycka-Roszak B
Comb Chem High Throughput Screen; 2010 Jan; 13(1):39-44. PubMed ID: 20201824
[TBL] [Abstract][Full Text] [Related]
10. Performance comparison of partial least squares-related variable selection methods for quantitative structure retention relationships modelling of retention times in reversed-phase liquid chromatography.
Talebi M; Schuster G; Shellie RA; Szucs R; Haddad PR
J Chromatogr A; 2015 Dec; 1424():69-76. PubMed ID: 26592563
[TBL] [Abstract][Full Text] [Related]
11. A QSPR study on the GC retention times of a series of fatty, dicarboxylic and amino acids by MLR and ANN.
Rouhollahi A; Shafieyan H; Ghasemi JB
Ann Chim; 2007 Sep; 97(9):925-33. PubMed ID: 17970308
[TBL] [Abstract][Full Text] [Related]
12. Prediction of octanol-water partition coefficients of organic compounds by multiple linear regression, partial least squares, and artificial neural network.
Golmohammadi H
J Comput Chem; 2009 Nov; 30(15):2455-65. PubMed ID: 19360793
[TBL] [Abstract][Full Text] [Related]
13. pK(a) modelling and prediction of drug molecules through GA-KPLS and L-M ANN.
Noorizadeh H; Farmany A; Noorizadeh M
Drug Test Anal; 2013 Feb; 5(2):103-9. PubMed ID: 21500371
[TBL] [Abstract][Full Text] [Related]
14. [Research on QSPR for n-octanol-water partition coefficients of organic compounds based on genetic algorithms-support vector machine and genetic algorithms-radial basis function neural networks].
Qi J; Niu JF; Wang LL
Huan Jing Ke Xue; 2008 Jan; 29(1):212-8. PubMed ID: 18441943
[TBL] [Abstract][Full Text] [Related]
15. Prediction of polar surface area of drug molecules: a QSPR approach.
Noorizadeh H; Farmany A; Noorizadeh M; Kohzadi M
Drug Test Anal; 2013 Apr; 5(4):222-7. PubMed ID: 21539000
[TBL] [Abstract][Full Text] [Related]
16. Building optimal regression tree by ant colony system-genetic algorithm: application to modeling of melting points.
Hemmateenejad B; Shamsipur M; Zare-Shahabadi V; Akhond M
Anal Chim Acta; 2011 Oct; 704(1-2):57-62. PubMed ID: 21907021
[TBL] [Abstract][Full Text] [Related]
17. Chemometrics-assisted simultaneous voltammetric determination of ascorbic acid, uric acid, dopamine and nitrite: application of non-bilinear voltammetric data for exploiting first-order advantage.
Gholivand MB; Jalalvand AR; Goicoechea HC; Skov T
Talanta; 2014 Feb; 119():553-63. PubMed ID: 24401455
[TBL] [Abstract][Full Text] [Related]
18. QSPR modeling of nonionic surfactant cloud points: an update.
Ren Y; Zhao B; Chang Q; Yao X
J Colloid Interface Sci; 2011 Jun; 358(1):202-7. PubMed ID: 21421217
[TBL] [Abstract][Full Text] [Related]
19. QSPR for the prediction of critical micelle concentration of different classes of surfactants using machine learning algorithms.
Boukelkal N; Rahal S; Rebhi R; Hamadache M
J Mol Graph Model; 2024 Jun; 129():108757. PubMed ID: 38503002
[TBL] [Abstract][Full Text] [Related]
20. QSPR estimation models of normal boiling point and relative liquid density of pure hydrocarbons using MLR and MLP-ANN methods.
Roubehie Fissa M; Lahiouel Y; Khaouane L; Hanini S
J Mol Graph Model; 2019 Mar; 87():109-120. PubMed ID: 30537641
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
